Keele Research Repository

The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or overlooked by existing methodologies. For the purpose of automating bridge visual inspection efficiently, this research examines and aids vision-based automated bridge component recognition. In this work, we propose a novel deep neural network-based attention model (DNNAM) architecture, which comprises synchronous dual attention modules (SDAM) and residual modules to recognise structural components. These modules help us to extract local discriminative features from structural component images and classify different categories of bridge components. These innovative modules are constructed at the contextual level of information encoding across spatial and channel dimensions.